Local registration is performed between consecutive frames \(I_{t-1}\)  and \(I_t\)  to compute a initial pose \(\tilde{\mathbf {T}}_t\) . To do so, correspondence between keyframes detected on each image is performed. Different from prior work [1]}, which relies on category-level 3D models to learn a fixed number of category-level semantic keypoints, this work aims to use generalizable features not specific to certain instances or categories. The LF-Net [2]} is chosen given its satisfactory balance between performance and inference speed. It only requires training on general 2D images, such as the ScanNet dataset [3]} used here, and generalizes to novel scenes. During testing, for the newly observed frame \(I_t\) , LF-Net receives the segmented image (Sec. REF ) as input. It then outputs \(n\)  keypoints \(x_i, i \in \lbrace 0,1,...,n-1\rbrace \)  along with the feature descriptor \(D_i \in R^{128}\) , where \(n\)  is 500 in all experiments. Due to the potentially imperfect segmentation in previous step, outlier keypoints can arise from the background. It is thus critical to perform feature matching and outlier pruning via RANSAC [4]}, executed in parallel on GPU in this work. Each registration sample consists of 3 pairs of keypoints matched between the two images. A pose hypothesis is generated from a sample via least squares [5]}. When evaluating samples, inlier correspondences have a distance between transformed point pairs below a threshold \(\delta \)  and an angle formed by the normals within a threshold \(\alpha \) . The values of \(\delta \)  and \(\alpha \)  are empirically set to \(5mm\)  and 45 in all experiments. After RANSAC, a preliminary pose is computed by \(\tilde{\mathbf {T}}_t=\mathbf {T}_{t-1} T_{t}^{t-1}\)  where \(T_{t}^{t-1}\)  is the best sampled correspondence hypothesis.
